add new files

app.py

@@ -1,5 +1,7 @@
 import os
 
+import pandas as pd
+
 os.system('cd fairseq;'
           'pip install ./; cd ..')
 
@@ -74,7 +76,7 @@ def draw_boxes(image, bounds, color='red', width=4):
     draw = ImageDraw.Draw(image)
     for i, bound in enumerate(bounds):
         p0, p1, p2, p3 = bound
-        draw.text(p0, str(i+1), fill=color)
+        draw.text(p0, str(i+1), fill=color, align='center')
         draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
     return image
 
@@ -179,22 +181,25 @@ def ocr(img):
 
         with torch.no_grad():
             result, scores = eval_step(task, generator, models, sample)
-        ocr_result.append(str(i+1) + '\t' + result[0]['ocr'].replace(' ', ''))
+        ocr_result.append([str(i+1), result[0]['ocr'].replace(' ', '')])
 
-    result = '\n'.join(ocr_result)
+    result = pd.DataFrame(ocr_result).iloc[:, 1:]
 
     return out_img, result
 
 
 title = "Chinese OCR"
-description = "Gradio Demo for Chinese OCR. Upload your own image or click any one of the examples, and click " \
-              "\"Submit\" and then wait for the generated OCR result. \n " \
-              "中文OCR体验区。欢迎上传图片，静待检测文字返回~"
+description = "Gradio Demo for Chinese OCR based on OFA. "\
+              "Upload your own image or click any one of the examples, and click " \
+              "\"Submit\" and then wait for the generated OCR result. " \
+              "\n中文OCR体验区。欢迎上传图片，静待检测文字返回~"
 article = "<p style='text-align: center'><a href='https://github.com/OFA-Sys/OFA' target='_blank'>OFA Github " \
           "Repo</a></p> "
-examples = [['shupai.png'], ['chinese.jpg'], ['gaidao.jpeg'], ['qiaodaima.png'], ['benpao.jpeg']]
+examples = [['shupai.png'], ['chinese.jpg'], ['gaidao.jpeg'], ['qiaodaima.png'],
+            ['benpao.jpeg'], ['wanli.png'], ['xsd.jpg']]
 io = gr.Interface(fn=ocr, inputs=gr.inputs.Image(type='filepath', label='Image'),
-                  outputs=[gr.outputs.Image(type='pil', label='Image'), gr.outputs.Textbox(label="OCR result")],
+                  outputs=[gr.outputs.Image(type='pil', label='Image'),
+                           gr.outputs.Dataframe(headers=['Box ID', 'Text'], label='OCR Results')],
                   title=title, description=description, article=article, examples=examples)
 io.launch()
 

requirements.txt

@@ -9,4 +9,5 @@ einops
 datasets
 python-Levenshtein
 zhconv
-transformers
+transformers
+pandas